import cv2
import numpy as np
import os

def load_images(folder):
    images = []
    file_names = []
    for filename in os.listdir(folder):
        file_names.append(filename)
        img = cv2.imread(os.path.join(folder, filename))
        if img is not None:
            images.append(img)
    return images, file_names

def tree_uprooting_detection(images, file_names):
    for i in range(1, len(images)):
        img1_gray = cv2.cvtColor(images[i - 1], cv2.COLOR_BGR2GRAY)
        img2_gray = cv2.cvtColor(images[i], cv2.COLOR_BGR2GRAY)

        diff = cv2.absdiff(img1_gray, img2_gray)
        _, thresh = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)

        kernel = np.ones((3, 3), np.uint8)
        dilated = cv2.dilate(thresh, kernel, iterations=3)

        contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours:
            (x, y, w, h) = cv2.boundingRect(contour)

            area = cv2.contourArea(contour)
            # Assuming an uprooted tree will result in a minimum of 100 square meters of change
            if area >= 400:
                print(file_names[i])
                #return True

    return False

def main(uid, polygon_id):
    folder = 'path/to/your/satellite/images'
    folder = uid + '/' + polygon_id
    images, file_names = load_images(folder)
    print(file_names)
    if tree_uprooting_detection(images, file_names):
        print('yes')
    else:
        print('no')

if __name__ == '__main__':
    main()